Image segmentation divides an image into meaningful regions for analysis. The three primary types are semantic segmentation, instance segmentation, and panoptic segmentation. Each serves distinct purposes and is applied in different scenarios, depending on whether the goal is to classify pixels, distinguish individual objects, or combine both approaches.
Semantic segmentation assigns a class label to every pixel in an image, grouping pixels into broad categories like “road,” “sky,” or “person.” For example, in autonomous driving systems, semantic segmentation helps identify drivable areas by labeling all road pixels. Models like U-Net or Fully Convolutional Networks (FCNs) are commonly used for this task. However, it doesn’t differentiate between multiple instances of the same class—two cars in an image would be labeled as “car” but not as separate objects.
Instance segmentation goes further by identifying and separating individual objects within a class. This is critical when counting or tracking distinct entities. For instance, in medical imaging, it can distinguish between overlapping cells in a microscope image. Mask R-CNN, a popular architecture, achieves this by combining object detection (to locate instances) with pixel-level masks. This approach is resource-intensive but provides granularity, making it useful in robotics or quality control systems where object-specific details matter.
Panoptic segmentation merges the two approaches, aiming to label every pixel with both a class and an instance ID where applicable. For example, in a street scene, it would label “road” as a semantic class and assign unique IDs to each car or pedestrian. Frameworks like Panoptic FPN (Feature Pyramid Network) tackle this by integrating semantic and instance segmentation branches. While computationally demanding, it’s valuable for applications requiring exhaustive scene understanding, such as advanced augmented reality or urban planning. Traditional methods like threshold-based or region-growing segmentation are simpler but lack the precision of deep learning-based techniques for complex tasks.